Open AccessBlock‐sparse recovery network for two‐dimensional harmonic retrieval
Author(s) -
Fu Rong,
Huang Tianyao,
Wang Lei,
Liu Yimin
Publication year - 2022
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12409
Subject(s) - block (permutation group theory) , computer science , harmonic , algorithm , reduction (mathematics) , signal (programming language) , sparse matrix , matrix (chemical analysis) , block matrix , mathematics , combinatorics , eigenvalues and eigenvectors , physics , geometry , materials science , quantum mechanics , composite material , gaussian , programming language
Block‐sparse signals, whose non‐zero entries appear in clusters, have received much attention recently. An unfolded network, named Ada‐BlockLISTA, was proposed to recover a block‐sparse signal at a small computational cost, which learns an individual weight matrix for each block. However, as the number of network parameters is increasingly associated with the number of blocks, the demand for parameter reduction becomes very significant, especially for large‐scale multidimensional harmonic retrieval (MHR) problems. Based on the dictionary characteristics in two‐dimensional (2D) harmonic retrieve problems, the authors introduce a weight coupling structure to shrink Ada‐BlockLISTA, which significantly reduces the number of weights without performance degradation. In simulations, the proposed block‐sparse reconstruction network, named AdaBLISTA‐CP, shows excellent recovery performance with a smaller number of learned parameters.